Weather-driven multi-category infrastructure impact forecasting

ABSTRACT

A method, system, and computer program product for resource management are described. The method includes selecting trouble regions within the service area, generating clustered regions, and training a trouble forecast model for the trouble regions for each type of damage, the training for each trouble region using training data from every trouble region within the clustered region associated with the trouble region. The method also includes applying the trouble forecast model for each trouble region within the service area for each type of damage, determining a trouble forecast for the service area for each type of damage based on the trouble forecast for each of the trouble regions within the service area, and determining a job forecast for the service area based on the trouble forecast for the service area, wherein the managing resources is based on the job forecast for the service area.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.17/224,116, filed on Apr. 6, 2021, entitled “Weather-DrivenMulti-Category Infrastructure Impact Forecasting,” which is acontinuation of U.S. patent application Ser. No. 16/546,268, filed onAug. 20, 2019, entitled “Weather-Driven Multi-Category InfrastructureImpact Forecasting,” issued as U.S. Pat. No. 11,048,021, which is acontinuation of U.S. patent application Ser. No. 15/287,846, filed onOct. 7, 2016, entitled “Weather-Driven Multi-Category InfrastructureImpact Forecasting,” issued as U.S. Pat. No. 10,387,802, which is acontinuation of U.S. patent application Ser. No. 15/075,603, filed Mar.21, 2016, entitled “Weather-Driven Multi-Category Infrastructure ImpactForecasting,” issued as U.S. Pat. No. 9,536,214, which is a continuationof U.S. application Ser. No. 15/002,494, filed on Jan. 21, 2016,entitled “Weather-Driven Multi-Category Infrastructure ImpactForecasting,” issued as U.S. Pat. No. 10,989,838, which claims priorityto U.S. Provisional Patent Application No. 62/147,003, filed on Apr. 14,2015, entitled “Forecasting Weather-Driven Infrastructure Impact,” allof which are incorporated in their entireties herein by reference.

BACKGROUND

The present invention relates to weather forecasting, and morespecifically, to weather-driven multi-category infrastructure impactforecasting.

Weather events can impact physical infrastructure in a number of ways.Power generation and distribution systems, water supply lines, gaspipelines, and telecommunication networks are exemplary systems that maybe impacted and require recovery and repair. Providers of services andutilities monitor weather forecasts to identify regions in whichinfrastructure may be impacted. By predicting areas where recover andrepair efforts may increase due to weather, the providers are able tomove equipment and personnel, as needed, to minimize the impact ofweather-related infrastructure damage.

SUMMARY

Embodiments include a computer-implemented method, system, and computerprogram product for managing resources based on weather-related damagein a service area. The method includes selecting trouble regions withinthe service area, generating clustered regions, each of the clusteredregions including at least one of the trouble regions within the servicearea and each of the trouble regions within the service area beingassociated with one of the clustered regions, and training a troubleforecast model for the trouble regions for each type of damage, thetraining for each trouble region using training data from every troubleregion within the clustered region associated with the trouble region.The method also includes applying the trouble forecast model for eachtrouble region within the service area for each type of damage,determining a trouble forecast for the service area for each type ofdamage based on the trouble forecast for each of the trouble regionswithin the service area, and determining a job forecast for the servicearea based on the trouble forecast for the service area, wherein themanaging resources is based on the job forecast for the service area.

Additional features and advantages are realized through the techniquesof the present invention. Other embodiments and aspects of the inventionare described in detail herein and are considered a part of the claimedinvention. For a better understanding of the invention with theadvantages and the features, refer to the description and to thedrawings.

BRIEF DESCRIPTION OF THE DRAWINGS

The subject matter which is regarded as the invention is particularlypointed out and distinctly claimed in the claims at the conclusion ofthe specification. The forgoing and other features, and advantages ofthe invention are apparent from the following detailed description takenin conjunction with the accompanying drawings in which:

FIG. 1 is a process flow of a method of performing forecasting accordingto embodiments;

FIG. 2 shows an exemplary cluster of trouble regions according to anembodiment;

FIG. 3 shows processes performed, on a per-trouble region basis, on the(historical) weather data collected by various sources according to anembodiment; and

FIG. 4 depicts a system to manage resources according to embodiments.

DETAILED DESCRIPTION

As noted above, weather events can impact infrastructure (e.g., a powergrid, pipeline, telecommunications network) such that repair orrebuilding is required to restore service. Forecasting the weatherevents facilitates planning and relocating equipment and personnel, asneeded, to restore service more quickly. Currently, weather forecastsare generally used to predict weather-related damage in two ways.Regression analysis may be used to model the number of damages oroutages as a function of predicted weather, and visualization methodsmay enable an expert to assess the risk of damage from predicted orcurrent weather. Current analysis techniques do not account for severalfactors that influence the efficacy of damage prediction. When theservice region or coverage area of service locations is large (e.g., 100square miles), there may be varying asset, geographic, and weathercharacteristics within the area. As such, the damage or trouble profilewithin the area may be different. There may be multiple different typesof trouble (damage) and types of jobs (repairs/reconstruction efforts).When a smaller area is analyzed to limit variations in geography andweather characteristics, historical data needed to train a model may besparse and, thus, result in no model or a bad model. Finally, combiningmultiple sources of weather observation and forecast may boost modelquality.

Embodiments of the systems and methods described herein account for thefactors noted above. According to the embodiments, trouble regions,which are smaller than a service region, are chosen and clusteredtogether according to their similarity, and sparse historical data amongtrouble regions of a cluster is grouped and scaled to normalizeinter-trouble region variations. Trouble types and job types areconsidered, different weather forecasts are combined. The embodimentsdetailed below relate to aggregating trouble region trouble forecasts inorder to compute service region trouble forecasts and, ultimately,service region job forecasts based on a trouble-to-job mapping.Generally, a single trouble forecast for a trouble region and a troubletype covers a window of time of duration T, which is referred to as theforecasting horizon. This forecasting horizon is divided into a set of ntime periods, which are referred to as prediction periods, each oflength Δ (e.g., 24 hours). For each trouble region, trouble type, andprediction period combination, a trouble count forecast is computed inthe form of a random variable with a probability distribution. Theprobability distribution may assign a probability of 1 to a singlevalue. The embodiments are detailed below.

FIG. 1 is a process flow of a method of performing forecasting accordingto embodiments. The forecasting may be a part of resource management fora utility or other enterprise. The forecasts of trouble andcorresponding jobs may be used to reallocate resources as needed toresolve weather-related issues. At block 110, selecting trouble regions210 (FIG. 2 ), B: {bi}, within each of the service regions 220 isillustrated in FIG. 2 . The trouble regions 210 increase spatialresolution within the service region 220. At block 120, clusteringtrouble regions 210 into clusters 230 (FIG. 2 ) includes groupingtrouble regions 210 whether or not they are in the same service region220. Clusters 230 are selected based on similarity in weather anddamage. At block 130, training a model for each trouble type (type ofdamage) for each trouble region 210 of the cluster 230 includes severalprocesses. The model is trained to forecast (weather-related) trouble ineach trouble region 210 and for each trouble type but may benefit fromusing historical and other data available within the cluster 230, asfurther detailed below. In order to train the model, at block 140,processing trouble history records includes scaling the trouble historyrecords to normalize inter-trouble region 210 variations as furtherdiscussed below. Processing historical weather data, at block 150,includes standardizing, interpolating and aggregating historical weatherdata to compute training input features as further detailed below. Atblock 160, calibrating training input features includes training andapplying a vector valued calibration function to the training inputfeatures to reduce systematic differences between training inputfeatures and scoring input features. At block 165, grouping trainingrecords includes grouping the training records of trouble regions 210for each cluster 230. When model training (block 130) is completed,applying a model, at block 170, includes spatially interpolating aweather forecast, at block 175, and computing scoring weather featureswith the interpolated data at block 180. Computing a service region 220trouble forecast, at block 190, includes aggregating trouble region 210trouble forecasts. At block 195, determining a service region 220 jobforecast includes applying a trouble-to-job mapping. The processesidentified in FIG. 1 are further detailed below.

FIG. 2 shows an exemplary cluster 230 of trouble regions 210 accordingto an embodiment. All the trouble regions 210 of a cluster 230 need notbe of the same size. Each service region 220 includes one or moreweather forecast grid points 240 according to the example shown in FIG.2 . However, a given service region 220 may not include any weatherforecast grid point. Each weather forecast grid point 240 is a point ona computational mesh used by the weather forecasting model. The weatherforecast model produces a time-dependent set of values (time series) formultiple weather features at each weather forecast grid point 240. Theweather features may include wind speed, wind direction, temperature,and pressure, for example. The weather forecast model may produce suchtime series for many (e.g., thousands) of weather forecast grid points240 that collectively cover an area (e.g., state, country). It isassumed that the coverage area of the weather forecast is mostlyoverlapping and completely covers the collective area of the serviceregions 220. Trouble regions 210 are subdivisions within a serviceregion 220 that facilitate a higher resolution view of the serviceregion 220. This is because the service region 220 is generally toolarge to experience homogenous weather (and, thus, damage). The troubleregions 210 may be selected in a number of ways and may be selectedbased on a desired size or resolution within each service region 220,for example. In the exemplary case of a power utility, trouble regions210 may be selected in association with substations or feeders. Troubleregions 210 may result from uniform random discretization, as well.Generally, the goal may be to choose trouble regions 210 such thatweather within each trouble region 210 is uniform. As noted above, whentoo small an area is chosen, sparse historical and current data maybecome an issue in generating accurate forecasts.

To overcome this issue, two or more trouble regions 210 may be combinedinto a cluster 230 and treated as one unit. A cluster 230 of troubleregions 210 may be selected in a number of ways to have variousdifferent similarities, for example. One exemplary algorithm forchoosing a cluster 230 is indicated below:

 D: Distance matrix, D(i, j) is distance between trouble regions i andj.  A: Adjacency matrix, A(i, j) = 1 if i, j are adjacent, else 0.  M: Avector of goodness metric for every trouble region. M(i) indicates  howgood the training data is for trouble region i.  T: A minimum requiredvalue of M for any trouble region  B: Set of all trouble regions  Q:Empty sorted map sorted by increasing value of the value MergeTroubleRegions (D, A, M, B, Q, T) {   for each (r in B) {    if(M(r) < T)      Q.insert (r, M (r))   } .   while (!Q.empty ( ) &&B.size ( ) > 1) {    (r,m) = Q.pop ( ) ;     r1 = r1 : min (D (r1,r)) &&A (r, r1) == 1 // nearest adjacent trouble region to r     rnew =Combine (r, r1)     Update D, A, M, Q // remove r, rl and add rnew if (M    (rnew) < T)       Q. insert (rnew, M (rnew))   } {The algorithm above includes adjacent trouble regions 210 in a cluster230 to achieve a minimum required goodness metric for training data. Anexemplary goodness metric is the number of records of trouble in thecombined trouble records of the trouble regions 210 in a cluster 230.Another exemplary goodness metric is the average number of troubleinstances per prediction period, computed over the prediction periodswith at least one instance of trouble, from the combined trouble recordsof the trouble regions 210 in a cluster 230. Other goodness metrics arepossible in alternate or additional embodiments. According to alternateembodiments, trouble regions 210 may be clustered based on similaritiesin location and geography. For a utility-based application, such as anelectric utility, similarities in demographics and the number of utilitypoles may additionally or alternately be used to form a cluster 230.While the use of trouble regions 210 provides spatial granularity, theuse of clusters 230 addresses any data sparseness issues.

As noted above, training a model (at block 130) involves processingtrouble history records (FIG. 1 , block 140). Processing the troublehistory records at block 140 refers to normalizing inter-trouble region210 variations. Normalizing inter-trouble region variations refers tothe multiplication of the trouble counts in each trouble record of atrouble region 210 with a scaling factor of that trouble region 210 asdescribed earlier. Trouble regions 210 within a single cluster 230 mayhave different trouble rates, meaning that for the same weather pattern,trouble region 210A may have, in general, higher instances of trouble(e.g. damage) than trouble region 210B. This may be caused by a varietyof factors, such as trouble region 210A having more equipment thantrouble region 210B, or trouble region 210A having older and weakerequipment than trouble region 210B, for example. To account for thisvariation in trouble rates between trouble regions 210 of the samecluster 230, a scaling factor may be used on the trouble counts obtainedin the trouble records for each trouble regions 210 of a cluster 230.The scaling factor may be different for each combination of troubleregion 210 and trouble type. It may be computed in a variety ofdifferent ways, such as the number of utility poles in each troubleregion 210, or the average number of trouble instances per day seen ineach trouble region 210, for example. The inverse of the scaling factorhas to be multiplied with the prediction of trouble count for eachtrouble region 210 during scoring.

Training the model also involves processing historical weather data(FIG. 1 , block 150). FIG. 3 shows processes performed, on a per-troubleregion 210 basis, on the (historical) weather data collected by varioussources 305 according to an embodiment. Historical weather data may beweather observation records obtained from a number of sources 305 a-305n (e.g., WeatherBug, a National Oceanographic and AtmosphericAdministration (NOAA) Metar station, an NOAA Mesonet station). Thedifferent sources 305 may have collected weather data at differentintervals and may have used different units. Historical weather data mayadditionally, or alternatively, be weather forecasts run for days inhistory. These historical weather data are comprised of time-varyingvalues for a variety of weather features, such as wind speed,temperature, pressure, for example, at certain geographical locations.In the case of observation data, the locations are where there areweather stations installed. In the case of weather forecasts, thelocations are the weather forecast grid points 240 (FIG. 2 ) on thecomputational mesh used by the weather forecast model. Thesegeographical locations are referred to as weather locations. Applyingfilters, at block 320 a-320 n, refers to filtering out low confidencedata with data quality filters. Each data quality filter may be specificto a given data source or may apply to multiple weather data sources.The standardizing, at block 330 a-330 n, includes converting all unitsto standard units (e.g., coordinated universal time (UTC) for time,Kelvin for temperature, meters per second (m/s) for wind speed,millimeters (mm) for precipitation, Pascal for pressure). Thetransforming, at block 340 a-340 n, refers to transforming thehistorical weather data from the different sources to common weatherfeatures (e.g., calculating relative humidity from temperature and dewpoint, calculating station pressure from sea level pressure andelevation). The normalizing, at block 350 a-350 n, involves normalizingthe various observation intervals of the various weather data sources toa common repeating time interval (e.g., hourly) resulting in anormalized time series for each weather feature at each weatherlocation. This common repeating time interval is of length δ and iscalled the normalized time step. The time points at which the normalizedtime series has values are called the normalized time points. Exemplaryhourly features include minimum temperature, maximum temperature,average wind speed, maximum wind speed, maximum wind gust, maximumhumidity, minimum pressure, maximum pressure, accumulated rain, and rainrate.

The interpolating, at block 360, refers to estimating the normalizedtime series for weather features of interest at a location that isrepresentative of a trouble region 210, for each trouble region 210 ofinterest. An example location of interest is the centroid of the polygonrepresenting the trouble region 210. For each trouble region 210, foreach weather feature of interest (e.g., temperature, rainfall), the timerange of training data is divided into aggregation time intervals oflength Δ. An example aggregation time interval length A is 24 hours (aday). For each such aggregation time interval, k weather locations areselected. The k locations may be observation stations or forecastlocations that are both nearest to the centroid of the trouble region210 and have a minimum number, p, of non-null values at the normalizedtime points within the aggregation time interval. As an example, p maybe 50% of Δ/δ, k may be 3 locations, but more or fewer locations may beselected based on the feature of interest, the application, and otherfactors. The feature of interest may affect the selection of k becausenot every observation or forecast station may record all the sameinformation. Thus, based on the feature of interest, a given station mayor may not be helpful. The selection of k may change over time from oneaggregation interval to another, as well. Each (training) weatherfeature of interest may then be computed at each normalized time pointwithin any given aggregation time interval as:

$\begin{matrix}{V = {\sum\limits_{i = 1}^{k}\frac{\frac{v(i)}{{distance}(i)}}{\sum\frac{1}{{distance}(i)}}}} & {{EQ}.1}\end{matrix}$

The value of the weather feature at weather location (i) is v(i), andthe distance from weather location (i) to the centroid of the troubleregion 210 is distance(i).

The aggregating, at block 370, refers to computing the model inputfeatures from the interpolated normalized time series for the weatherfeatures of interest, by applying the appropriate aggregation functionto the normalized time series, for each aggregation interval. Examplesof aggregation functions are minimum, maximum, average, accumulate, andnumber of exceedences over a threshold. Example input features are thedaily maximum temperature, daily minimum temperature, daily minimumpressure, daily maximum wind gust speed, daily maximum reflectivity,number of exceedences of wind gust speed over 40 mph in a day, and monthof the year. An input feature vector is computed for each aggregationperiod in the training data time range, and for each trouble region 210.Each input feature vector is a collection of values, with one value foreach input feature. The resulting collection of input feature vectors iscalled the training input feature data set. The combination of atraining input feature vector and the corresponding trouble count for asingle trouble type from the trouble record of the same trouble region210, and for the same prediction period, is called a training record.

As discussed above, training input features may be computed, at least inpart, using weather observations. Scoring input features are computedfrom weather forecasts. To remove any systematic differences betweentraining input features and scoring input features, the training inputsfeatures may be calibrated (at block 160, FIG. 1 ) against the scoringinput features. The vector-valued function may be trained using anystatistical inference or machine learning techniques. For example, thevector-valued function may be a multi-variate linear model that istrained using multivariate multiple linear regression. This function canbe trained on a calibration data set that includes scoring input featurevectors and training input feature vectors for the same predictionperiods. These scoring input feature vectors may be generated speciallyfor this calibration process, or may be generated as part of the normalscoring performed by the system, and then applied into the calibrationprocess. The vector-valued function is then applied to every inputfeature vector in the training input feature data set to compute thecalibrated training input feature data set. The combination of acalibrated training input feature vector and the corresponding troublecount for a single trouble type from the trouble record of the sametrouble region 210, and for the same prediction period, is called acalibrated training record.

Grouping the training records (block 165, FIG. 1 ) refers to thegrouping of calibrated training records (from block 160) of troubleregions 210 in each cluster 230. This results in a group of trainingrecords for each cluster 230 and trouble type. Each such group can bethen used to train a trouble model for the corresponding cluster 230 andtrouble type.

As noted in the discussion of FIG. 1 , the processes involved inforecasting according to embodiments described herein include training a(trouble forecasting) model for each combination of trouble region 210(FIG. 1 , block 130) and trouble type. The model may be a two-part modelaccording to an exemplary embodiment. The first part may involve troubleclassification based on a decision tree, support vector machine (SVM),or neural network, for example. At a given time step for each troubletype, the trouble classification indicates whether the trouble processis triggered. Trouble types may be specific to the application. In theexemplary application of a power utility, trouble types may include asingle-customer outage, a multi-customer outage, and a downed wirewithout any outage. Trouble types may be thought of as types of damage.Thus, in the exemplary power utility application, other exemplarytrouble types may involve pole damage due to trees and transformerfailure. In airline operations applications, trouble types may includeflight delays, icing, and plane technical failure, for example. Inagriculture, for example, trouble types can refer to crop damage due toextreme weather events like hail. As discussed below, a trouble type maybe associated with one or more jobs of one or more given job types ormay not be associated with a job. The second part of the two-part modelmay involve a trouble count regression based on a generalized linearmodel such as a Poisson, negative binomial, or binomial model. For atrouble process (of a trouble type in a given prediction period) thathas been triggered, the trouble count regression indicates the number ofinstances of trouble (of the trouble type) that are expected.

Once the (trouble forecasting) model is trained for a given troubleregion 210 and trouble type, applying the model (FIG. 1, 170 ) providesthe trouble forecast as a time series D_(t). To apply the model, weatherforecast data (taken as a time series F_(t)) is spatially interpolatedto centroids of the trouble region or regions 210 of interest. Scoringinput features are computed from the interpolated weather forecast ateach centroid (each trouble region 210 of interest). For each troubleregion 210, trouble type, and prediction period in the forecastinghorizon, the trouble model for that trouble region 210 and trouble typeis evaluated on the scoring input features for that trouble region 210.The result is a probability distribution of a trouble count randomvariable for that trouble region 210, trouble type and predictionperiod. This random variable is then divided by the scaling factor forthe combination of this trouble region 210 and trouble type, resultingin a scaled random variable. This scaled random variable is the troubleforecast for the same trouble region 210, trouble type and predictionperiod. This computation is applied for all combinations of troubleregions 210, trouble types and prediction periods in the forecastinghorizon, that are of interest. Aggregating trouble forecasts D_(t) oftrouble regions 210 within a service region 220 for each trouble typeprovides the trouble forecast E_(t) for the service region 220 (FIG. 1,190 ) for each trouble type. The aggregating may include addition ofPoisson random variables by adding mean values. The aggregating mayinstead include convolution using numerical quadrature methods tocompute the probability density function of the aggregated troubleforecast. Finally, with the trouble forecast for each trouble type for agiven service region 220, the job forecast J for the service region 220may be determined (FIG. 1, 195 ) based on a trouble-to-job type mapping.The trouble forecast and job forecast may be used in resource managementto significantly improve the time to resolve weather related serviceinterruptions and infrastructure damage.

FIG. 4 depicts a system 400 to manage resources according to embodimentsdetailed herein. The system 400 receives inputs of weather data from anumber of sources 305. The system 400 includes one or more memorydevices 410 to store instructions and data. The system 400 also includesone or more processors 420 to perform the processes discussed above totrain a trouble forecasting model and ultimately output trouble and jobforecasts. The system 400 may additionally include known display devicesand interfaces to receive and output information. The information fromthe system 400 may be provided as output 430 to a display device, acontroller of the resources or the like so that actions may be taken toaddress the damage and required jobs.

The present invention may be a system, a method, and/or a computerprogram product at any possible technical detail level of integration.The computer program product may include a computer readable storagemedium (or media) having computer readable program instructions thereonfor causing a processor to carry out aspects of the present invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, configuration data for integrated circuitry, oreither source code or object code written in any combination of one ormore programming languages, including an object oriented programminglanguage such as Smalltalk, C++, or the like, and procedural programminglanguages, such as the “C” programming language or similar programminglanguages. The computer readable program instructions may executeentirely on the user's computer, partly on the user's computer, as astand-alone software package, partly on the user's computer and partlyon a remote computer or entirely on the remote computer or server. Inthe latter scenario, the remote computer may be connected to the user'scomputer through any type of network, including a local area network(LAN) or a wide area network (WAN), or the connection may be made to anexternal computer (for example, through the Internet using an InternetService Provider). In some embodiments, electronic circuitry including,for example, programmable logic circuitry, field-programmable gatearrays (FPGA), or programmable logic arrays (PLA) may execute thecomputer readable program instructions by utilizing state information ofthe computer readable program instructions to personalize the electroniccircuitry, in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the blocks may occur out of theorder noted in the Figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

The terminology used herein is for the purpose of describing particularembodiments only and is not intended to be limiting of the invention. Asused herein, the singular forms “a”, “an” and “the” are intended toinclude the plural forms as well, unless the context clearly indicatesotherwise. It will be further understood that the terms “comprises”and/or “comprising,” when used in this specification, specify thepresence of stated features, integers, steps, operations, elements,and/or components, but do not preclude the presence or addition of onemore other features, integers, steps, operations, element components,and/or groups thereof

The corresponding structures, materials, acts, and equivalents of allmeans or step plus function elements in the claims below are intended toinclude any structure, material, or act for performing the function incombination with other claimed elements as specifically claimed. Thedescription of the present invention has been presented for purposes ofillustration and description, but is not intended to be exhaustive orlimited to the invention in the form disclosed. Many modifications andvariations will be apparent to those of ordinary skill in the artwithout departing from the scope and spirit of the invention. Theembodiment was chosen and described in order to best explain theprinciples of the invention and the practical application, and to enableothers of ordinary skill in the art to understand the invention forvarious embodiments with various modifications as are suited to theparticular use contemplated.

The flow diagrams depicted herein are just one example. There may bemany variations to this diagram or the steps (or operations) describedtherein without departing from the spirit of the invention. Forinstance, the steps may be performed in a differing order or steps maybe added, deleted or modified. All of these variations are considered apart of the claimed invention.

While the preferred embodiment to the invention had been described, itwill be understood that those skilled in the art, both now and in thefuture, may make various improvements and enhancements which fall withinthe scope of the claims which follow. These claims should be construedto maintain the proper protection for the invention first described.

The descriptions of the various embodiments of the present inventionhave been presented for purposes of illustration, but are not intendedto be exhaustive or limited to the embodiments disclosed. Manymodifications and variations will be apparent to those of ordinary skillin the art without departing from the scope and spirit of the describedembodiments. The terminology used herein was chosen to best explain theprinciples of the embodiments, the practical application or technicalimprovement over technologies found in the marketplace, or to enableothers of ordinary skill in the art to understand the embodimentsdisclosed herein.

What is claimed is:
 1. A non-transitory computer program product, thenon-transitory computer program product comprising a computer readablestorage medium having instructions embodied therewith, the instructionsexecutable by at least one processor to perform a method comprising:selecting a plurality of trouble regions within a service area;generating clustered regions, each of the clustered regions including atleast one of the trouble regions within the service area and each of thetrouble regions within the service area being associated with one of theclustered regions, the at least one of the trouble region of eachcluster region being generated based on a distance between the at leastone of the trouble region and other trouble regions of the plurality oftrouble regions within the service area; for each of the troubleregions: training a plurality of trouble forecast models for each typeof weather-related damage, each of the plurality of trouble forecastmodel being for a different type of weather-related damage, the trainingfor each of the plurality of trouble forecast models including:receiving historical weather data from a plurality of weather datasources, the historical weather data being from at least a portion ofthe service area including a particular trouble region; filtering outlow confidence historical weather data with data quality filters, eachof the quality filters being specific to one of the plurality of weatherdata sources; and normalizing the historical weather data for aparticular weather-related damage to a common repeating time interval toobtain a normalized time series for a particular weather feature, thenormalized being based on one of a minimum, maximum, an average, or arate of change; and applying each of the trouble forecast models for theat least one clustered region within the service area to obtain atrouble forecast for the particular trouble region within the servicearea for each type of the weather-related damage; determining a regionalforecast for a geographic service area based on the trouble forecast forthe geographic service area in order to facilitate management ofresources by relocating equipment and personnel within the service area;providing the regional forecast for the geographic service area based onthe trouble forecast, in order to minimize an impact of the troubleforecast.